{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Convolutional Networks\n",
    "So far we have worked with deep fully-connected networks, using them to explore different optimization strategies and network architectures. Fully-connected networks are a good testbed for experimentation because they are very computationally efficient, but in practice all state-of-the-art results use convolutional networks instead.\n",
    "\n",
    "First you will implement several layer types that are used in convolutional networks. You will then use these layers to train a convolutional network on the CIFAR-10 dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# As usual, a bit of setup\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from cs231n.classifiers.cnn import *\n",
    "from cs231n.data_utils import get_CIFAR10_data\n",
    "from cs231n.gradient_check import eval_numerical_gradient_array, eval_numerical_gradient\n",
    "from cs231n.layers import *\n",
    "from cs231n.fast_layers import *\n",
    "from cs231n.solver import Solver\n",
    "\n",
    "%matplotlib inline\n",
    "plt.rcParams['figure.figsize'] = (10.0, 8.0) # set default size of plots\n",
    "plt.rcParams['image.interpolation'] = 'nearest'\n",
    "plt.rcParams['image.cmap'] = 'gray'\n",
    "\n",
    "# for auto-reloading external modules\n",
    "# see http://stackoverflow.com/questions/1907993/autoreload-of-modules-in-ipython\n",
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "\n",
    "def rel_error(x, y):\n",
    "  \"\"\" returns relative error \"\"\"\n",
    "  return np.max(np.abs(x - y) / (np.maximum(1e-8, np.abs(x) + np.abs(y))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_val:  (1000, 3, 32, 32)\n",
      "X_train:  (49000, 3, 32, 32)\n",
      "X_test:  (1000, 3, 32, 32)\n",
      "y_val:  (1000,)\n",
      "y_train:  (49000,)\n",
      "y_test:  (1000,)\n"
     ]
    }
   ],
   "source": [
    "# Load the (preprocessed) CIFAR10 data.\n",
    "\n",
    "data = get_CIFAR10_data()\n",
    "for k, v in data.iteritems():\n",
    "  print '%s: ' % k, v.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Convolution: Naive forward pass\n",
    "The core of a convolutional network is the convolution operation. In the file `cs231n/layers.py`, implement the forward pass for the convolution layer in the function `conv_forward_naive`. \n",
    "\n",
    "You don't have to worry too much about efficiency at this point; just write the code in whatever way you find most clear.\n",
    "\n",
    "You can test your implementation by running the following:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing conv_forward_naive\n",
      "difference:  2.21214764175e-08\n"
     ]
    }
   ],
   "source": [
    "x_shape = (2, 3, 4, 4)\n",
    "w_shape = (3, 3, 4, 4)\n",
    "x = np.linspace(-0.1, 0.5, num=np.prod(x_shape)).reshape(x_shape)\n",
    "w = np.linspace(-0.2, 0.3, num=np.prod(w_shape)).reshape(w_shape)\n",
    "b = np.linspace(-0.1, 0.2, num=3)\n",
    "\n",
    "conv_param = {'stride': 2, 'pad': 1}\n",
    "out, _ = conv_forward_naive(x, w, b, conv_param)\n",
    "correct_out = np.array([[[[[-0.08759809, -0.10987781],\n",
    "                           [-0.18387192, -0.2109216 ]],\n",
    "                           [[ 0.21027089,  0.21661097],\n",
    "                           [ 0.22847626,  0.23004637]],\n",
    "                          [[ 0.50813986,  0.54309974],\n",
    "                           [ 0.64082444,  0.67101435]]],\n",
    "                         [[[-0.98053589, -1.03143541],\n",
    "                           [-1.19128892, -1.24695841]],\n",
    "                          [[ 0.69108355,  0.66880383],\n",
    "                           [ 0.59480972,  0.56776003]],\n",
    "                          [[ 2.36270298,  2.36904306],\n",
    "                           [ 2.38090835,  2.38247847]]]]])\n",
    "\n",
    "# Compare your output to ours; difference should be around 1e-8\n",
    "print 'Testing conv_forward_naive'\n",
    "print 'difference: ', rel_error(out, correct_out)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Aside: Image processing via convolutions\n",
    "\n",
    "As fun way to both check your implementation and gain a better understanding of the type of operation that convolutional layers can perform, we will set up an input containing two images and manually set up filters that perform common image processing operations (grayscale conversion and edge detection). The convolution forward pass will apply these operations to each of the input images. We can then visualize the results as a sanity check."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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IeBRxAuJRHz5Rg4rHmKYHWmBQuqDgTUdjlelU6O4pjSGAKhTROUYmGAmMjo2V2i1Inxie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oZiNpsxGo3MOiBsLxzJVAhYy4L1LFC3yZGn9YH92iKwk232b5Lfm71fJ33eHh+LYuiedK32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0IkBn+0taOmW3YNjudJDO5rFZTNgNZkrFPFvrq0TmZmhWKuwZHMTr96A3m+gZGKGUL6Cz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\n/+/r/w7bJhaHW5wQehNyhTzpbBZfgZPJ4qRSuDNIF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UPZ4TsFx++KEtR9gbrr0FmDi8vn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\nZJJJJpnkC8gEuiaZZJJJJplkkkm+gPz/jOCxAzObs1EAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9c90c490d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from scipy.misc import imread, imresize\n",
    "\n",
    "kitten, puppy = imread('kitten.jpg'), imread('puppy.jpg')\n",
    "# kitten is wide, and puppy is already square\n",
    "d = kitten.shape[1] - kitten.shape[0]\n",
    "kitten_cropped = kitten[:, d/2:-d/2, :]\n",
    "\n",
    "img_size = 200   # Make this smaller if it runs too slow\n",
    "x = np.zeros((2, 3, img_size, img_size))\n",
    "x[0, :, :, :] = imresize(puppy, (img_size, img_size)).transpose((2, 0, 1))\n",
    "x[1, :, :, :] = imresize(kitten_cropped, (img_size, img_size)).transpose((2, 0, 1))\n",
    "\n",
    "# Set up a convolutional weights holding 2 filters, each 3x3\n",
    "w = np.zeros((2, 3, 3, 3))\n",
    "\n",
    "# The first filter converts the image to grayscale.\n",
    "# Set up the red, green, and blue channels of the filter.\n",
    "w[0, 0, :, :] = [[0, 0, 0], [0, 0.3, 0], [0, 0, 0]]\n",
    "w[0, 1, :, :] = [[0, 0, 0], [0, 0.6, 0], [0, 0, 0]]\n",
    "w[0, 2, :, :] = [[0, 0, 0], [0, 0.1, 0], [0, 0, 0]]\n",
    "\n",
    "# Second filter detects horizontal edges in the blue channel.\n",
    "w[1, 2, :, :] = [[1, 2, 1], [0, 0, 0], [-1, -2, -1]]\n",
    "\n",
    "# Vector of biases. We don't need any bias for the grayscale\n",
    "# filter, but for the edge detection filter we want to add 128\n",
    "# to each output so that nothing is negative.\n",
    "b = np.array([0, 128])\n",
    "\n",
    "# Compute the result of convolving each input in x with each filter in w,\n",
    "# offsetting by b, and storing the results in out.\n",
    "out, _ = conv_forward_naive(x, w, b, {'stride': 1, 'pad': 1})\n",
    "\n",
    "def imshow_noax(img, normalize=True):\n",
    "    \"\"\" Tiny helper to show images as uint8 and remove axis labels \"\"\"\n",
    "    if normalize:\n",
    "        img_max, img_min = np.max(img), np.min(img)\n",
    "        img = 255.0 * (img - img_min) / (img_max - img_min)\n",
    "    plt.imshow(img.astype('uint8'))\n",
    "    plt.gca().axis('off')\n",
    "\n",
    "# Show the original images and the results of the conv operation\n",
    "plt.subplot(2, 3, 1)\n",
    "imshow_noax(puppy, normalize=False)\n",
    "plt.title('Original image')\n",
    "plt.subplot(2, 3, 2)\n",
    "imshow_noax(out[0, 0])\n",
    "plt.title('Grayscale')\n",
    "plt.subplot(2, 3, 3)\n",
    "imshow_noax(out[0, 1])\n",
    "plt.title('Edges')\n",
    "plt.subplot(2, 3, 4)\n",
    "imshow_noax(kitten_cropped, normalize=False)\n",
    "plt.subplot(2, 3, 5)\n",
    "imshow_noax(out[1, 0])\n",
    "plt.subplot(2, 3, 6)\n",
    "imshow_noax(out[1, 1])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Convolution: Naive backward pass\n",
    "Implement the backward pass for the convolution operation in the function `conv_backward_naive` in the file `cs231n/layers.py`. Again, you don't need to worry too much about computational efficiency.\n",
    "\n",
    "When you are done, run the following to check your backward pass with a numeric gradient check."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing conv_backward_naive function\n",
      "dx error:  1.26207696767e-09\n",
      "dw error:  1.01581219123e-09\n",
      "db error:  1.3561551842e-11\n"
     ]
    }
   ],
   "source": [
    "x = np.random.randn(4, 3, 5, 5)\n",
    "w = np.random.randn(2, 3, 3, 3)\n",
    "b = np.random.randn(2,)\n",
    "dout = np.random.randn(4, 2, 5, 5)\n",
    "conv_param = {'stride': 1, 'pad': 1}\n",
    "\n",
    "dx_num = eval_numerical_gradient_array(lambda x: conv_forward_naive(x, w, b, conv_param)[0], x, dout)\n",
    "dw_num = eval_numerical_gradient_array(lambda w: conv_forward_naive(x, w, b, conv_param)[0], w, dout)\n",
    "db_num = eval_numerical_gradient_array(lambda b: conv_forward_naive(x, w, b, conv_param)[0], b, dout)\n",
    "\n",
    "out, cache = conv_forward_naive(x, w, b, conv_param)\n",
    "dx, dw, db = conv_backward_naive(dout, cache)\n",
    "\n",
    "# Your errors should be around 1e-9'\n",
    "print 'Testing conv_backward_naive function'\n",
    "print 'dx error: ', rel_error(dx, dx_num)\n",
    "print 'dw error: ', rel_error(dw, dw_num)\n",
    "print 'db error: ', rel_error(db, db_num)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Max pooling: Naive forward\n",
    "Implement the forward pass for the max-pooling operation in the function `max_pool_forward_naive` in the file `cs231n/layers.py`. Again, don't worry too much about computational efficiency.\n",
    "\n",
    "Check your implementation by running the following:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing max_pool_forward_naive function:\n",
      "difference:  4.16666651573e-08\n"
     ]
    }
   ],
   "source": [
    "x_shape = (2, 3, 4, 4)\n",
    "x = np.linspace(-0.3, 0.4, num=np.prod(x_shape)).reshape(x_shape)\n",
    "pool_param = {'pool_width': 2, 'pool_height': 2, 'stride': 2}\n",
    "\n",
    "out, _ = max_pool_forward_naive(x, pool_param)\n",
    "\n",
    "correct_out = np.array([[[[-0.26315789, -0.24842105],\n",
    "                          [-0.20421053, -0.18947368]],\n",
    "                         [[-0.14526316, -0.13052632],\n",
    "                          [-0.08631579, -0.07157895]],\n",
    "                         [[-0.02736842, -0.01263158],\n",
    "                          [ 0.03157895,  0.04631579]]],\n",
    "                        [[[ 0.09052632,  0.10526316],\n",
    "                          [ 0.14947368,  0.16421053]],\n",
    "                         [[ 0.20842105,  0.22315789],\n",
    "                          [ 0.26736842,  0.28210526]],\n",
    "                         [[ 0.32631579,  0.34105263],\n",
    "                          [ 0.38526316,  0.4       ]]]])\n",
    "\n",
    "# Compare your output with ours. Difference should be around 1e-8.\n",
    "print 'Testing max_pool_forward_naive function:'\n",
    "print 'difference: ', rel_error(out, correct_out)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Max pooling: Naive backward\n",
    "Implement the backward pass for the max-pooling operation in the function `max_pool_backward_naive` in the file `cs231n/layers.py`. You don't need to worry about computational efficiency.\n",
    "\n",
    "Check your implementation with numeric gradient checking by running the following:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing max_pool_backward_naive function:\n",
      "dx error:  3.27565352145e-12\n"
     ]
    }
   ],
   "source": [
    "x = np.random.randn(3, 2, 8, 8)\n",
    "dout = np.random.randn(3, 2, 4, 4)\n",
    "pool_param = {'pool_height': 2, 'pool_width': 2, 'stride': 2}\n",
    "\n",
    "dx_num = eval_numerical_gradient_array(lambda x: max_pool_forward_naive(x, pool_param)[0], x, dout)\n",
    "\n",
    "out, cache = max_pool_forward_naive(x, pool_param)\n",
    "dx = max_pool_backward_naive(dout, cache)\n",
    "\n",
    "# Your error should be around 1e-12\n",
    "print 'Testing max_pool_backward_naive function:'\n",
    "print 'dx error: ', rel_error(dx, dx_num)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Fast layers\n",
    "Making convolution and pooling layers fast can be challenging. To spare you the pain, we've provided fast implementations of the forward and backward passes for convolution and pooling layers in the file `cs231n/fast_layers.py`.\n",
    "\n",
    "The fast convolution implementation depends on a Cython extension; to compile it you need to run the following from the `cs231n` directory:\n",
    "\n",
    "```bash\n",
    "python setup.py build_ext --inplace\n",
    "```\n",
    "\n",
    "The API for the fast versions of the convolution and pooling layers is exactly the same as the naive versions that you implemented above: the forward pass receives data, weights, and parameters and produces outputs and a cache object; the backward pass recieves upstream derivatives and the cache object and produces gradients with respect to the data and weights.\n",
    "\n",
    "**NOTE:** The fast implementation for pooling will only perform optimally if the pooling regions are non-overlapping and tile the input. If these conditions are not met then the fast pooling implementation will not be much faster than the naive implementation.\n",
    "\n",
    "You can compare the performance of the naive and fast versions of these layers by running the following:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing conv_forward_fast:\n",
      "Naive: 0.158119s\n",
      "Fast: 0.013810s\n",
      "Speedup: 11.449468x\n",
      "Difference:  1.14161965283e-10\n",
      "\n",
      "Testing conv_backward_fast:\n",
      "Naive: 0.442712s\n",
      "Fast: 0.012921s\n",
      "Speedup: 34.262718x\n",
      "dx difference:  1.26798089179e-12\n",
      "dw difference:  4.52425721901e-12\n",
      "db difference:  0.0\n"
     ]
    }
   ],
   "source": [
    "from cs231n.fast_layers import conv_forward_fast, conv_backward_fast\n",
    "from time import time\n",
    "\n",
    "x = np.random.randn(100, 3, 31, 31)\n",
    "w = np.random.randn(25, 3, 3, 3)\n",
    "b = np.random.randn(25,)\n",
    "dout = np.random.randn(100, 25, 16, 16)\n",
    "conv_param = {'stride': 2, 'pad': 1}\n",
    "\n",
    "t0 = time()\n",
    "out_naive, cache_naive = conv_forward_naive(x, w, b, conv_param)\n",
    "t1 = time()\n",
    "out_fast, cache_fast = conv_forward_fast(x, w, b, conv_param)\n",
    "t2 = time()\n",
    "\n",
    "print 'Testing conv_forward_fast:'\n",
    "print 'Naive: %fs' % (t1 - t0)\n",
    "print 'Fast: %fs' % (t2 - t1)\n",
    "print 'Speedup: %fx' % ((t1 - t0) / (t2 - t1))\n",
    "print 'Difference: ', rel_error(out_naive, out_fast)\n",
    "\n",
    "t0 = time()\n",
    "dx_naive, dw_naive, db_naive = conv_backward_naive(dout, cache_naive)\n",
    "t1 = time()\n",
    "dx_fast, dw_fast, db_fast = conv_backward_fast(dout, cache_fast)\n",
    "t2 = time()\n",
    "\n",
    "print '\\nTesting conv_backward_fast:'\n",
    "print 'Naive: %fs' % (t1 - t0)\n",
    "print 'Fast: %fs' % (t2 - t1)\n",
    "print 'Speedup: %fx' % ((t1 - t0) / (t2 - t1))\n",
    "print 'dx difference: ', rel_error(dx_naive, dx_fast)\n",
    "print 'dw difference: ', rel_error(dw_naive, dw_fast)\n",
    "print 'db difference: ', rel_error(db_naive, db_fast)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing pool_forward_fast:\n",
      "Naive: 0.015695s\n",
      "fast: 0.002080s\n",
      "speedup: 7.545736x\n",
      "difference:  0.0\n",
      "\n",
      "Testing pool_backward_fast:\n",
      "Naive: 0.017107s\n",
      "speedup: 1.669156x\n",
      "dx difference:  0.0\n"
     ]
    }
   ],
   "source": [
    "from cs231n.fast_layers import max_pool_forward_fast, max_pool_backward_fast\n",
    "\n",
    "x = np.random.randn(100, 3, 32, 32)\n",
    "dout = np.random.randn(100, 3, 16, 16)\n",
    "pool_param = {'pool_height': 2, 'pool_width': 2, 'stride': 2}\n",
    "\n",
    "t0 = time()\n",
    "out_naive, cache_naive = max_pool_forward_naive(x, pool_param)\n",
    "t1 = time()\n",
    "out_fast, cache_fast = max_pool_forward_fast(x, pool_param)\n",
    "t2 = time()\n",
    "\n",
    "print 'Testing pool_forward_fast:'\n",
    "print 'Naive: %fs' % (t1 - t0)\n",
    "print 'fast: %fs' % (t2 - t1)\n",
    "print 'speedup: %fx' % ((t1 - t0) / (t2 - t1))\n",
    "print 'difference: ', rel_error(out_naive, out_fast)\n",
    "\n",
    "t0 = time()\n",
    "dx_naive = max_pool_backward_naive(dout, cache_naive)\n",
    "t1 = time()\n",
    "dx_fast = max_pool_backward_fast(dout, cache_fast)\n",
    "t2 = time()\n",
    "\n",
    "print '\\nTesting pool_backward_fast:'\n",
    "print 'Naive: %fs' % (t1 - t0)\n",
    "print 'speedup: %fx' % ((t1 - t0) / (t2 - t1))\n",
    "print 'dx difference: ', rel_error(dx_naive, dx_fast)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Convolutional \"sandwich\" layers\n",
    "Previously we introduced the concept of \"sandwich\" layers that combine multiple operations into commonly used patterns. In the file `cs231n/layer_utils.py` you will find sandwich layers that implement a few commonly used patterns for convolutional networks."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing conv_relu_pool\n",
      "dx error:  1.41107528806e-08\n",
      "dw error:  2.40094836959e-09\n",
      "db error:  2.22216810734e-11\n"
     ]
    }
   ],
   "source": [
    "from cs231n.layer_utils import conv_relu_pool_forward, conv_relu_pool_backward\n",
    "\n",
    "x = np.random.randn(2, 3, 16, 16)\n",
    "w = np.random.randn(3, 3, 3, 3)\n",
    "b = np.random.randn(3,)\n",
    "dout = np.random.randn(2, 3, 8, 8)\n",
    "conv_param = {'stride': 1, 'pad': 1}\n",
    "pool_param = {'pool_height': 2, 'pool_width': 2, 'stride': 2}\n",
    "\n",
    "out, cache = conv_relu_pool_forward(x, w, b, conv_param, pool_param)\n",
    "dx, dw, db = conv_relu_pool_backward(dout, cache)\n",
    "\n",
    "dx_num = eval_numerical_gradient_array(lambda x: conv_relu_pool_forward(x, w, b, conv_param, pool_param)[0], x, dout)\n",
    "dw_num = eval_numerical_gradient_array(lambda w: conv_relu_pool_forward(x, w, b, conv_param, pool_param)[0], w, dout)\n",
    "db_num = eval_numerical_gradient_array(lambda b: conv_relu_pool_forward(x, w, b, conv_param, pool_param)[0], b, dout)\n",
    "\n",
    "print 'Testing conv_relu_pool'\n",
    "print 'dx error: ', rel_error(dx_num, dx)\n",
    "print 'dw error: ', rel_error(dw_num, dw)\n",
    "print 'db error: ', rel_error(db_num, db)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing conv_relu:\n",
      "dx error:  4.65303588698e-09\n",
      "dw error:  1.8778124723e-10\n",
      "db error:  2.48971775654e-11\n"
     ]
    }
   ],
   "source": [
    "from cs231n.layer_utils import conv_relu_forward, conv_relu_backward\n",
    "\n",
    "x = np.random.randn(2, 3, 8, 8)\n",
    "w = np.random.randn(3, 3, 3, 3)\n",
    "b = np.random.randn(3,)\n",
    "dout = np.random.randn(2, 3, 8, 8)\n",
    "conv_param = {'stride': 1, 'pad': 1}\n",
    "\n",
    "out, cache = conv_relu_forward(x, w, b, conv_param)\n",
    "dx, dw, db = conv_relu_backward(dout, cache)\n",
    "\n",
    "dx_num = eval_numerical_gradient_array(lambda x: conv_relu_forward(x, w, b, conv_param)[0], x, dout)\n",
    "dw_num = eval_numerical_gradient_array(lambda w: conv_relu_forward(x, w, b, conv_param)[0], w, dout)\n",
    "db_num = eval_numerical_gradient_array(lambda b: conv_relu_forward(x, w, b, conv_param)[0], b, dout)\n",
    "\n",
    "print 'Testing conv_relu:'\n",
    "print 'dx error: ', rel_error(dx_num, dx)\n",
    "print 'dw error: ', rel_error(dw_num, dw)\n",
    "print 'db error: ', rel_error(db_num, db)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Three-layer ConvNet\n",
    "Now that you have implemented all the necessary layers, we can put them together into a simple convolutional network.\n",
    "\n",
    "Open the file `cs231n/cnn.py` and complete the implementation of the `ThreeLayerConvNet` class. Run the following cells to help you debug:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Sanity check loss\n",
    "After you build a new network, one of the first things you should do is sanity check the loss. When we use the softmax loss, we expect the loss for random weights (and no regularization) to be about `log(C)` for `C` classes. When we add regularization this should go up."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Initial loss (no regularization):  2.30258247955\n",
      "Initial loss (with regularization):  2.50895140274\n"
     ]
    }
   ],
   "source": [
    "model = ThreeLayerConvNet()\n",
    "\n",
    "N = 50\n",
    "X = np.random.randn(N, 3, 32, 32)\n",
    "y = np.random.randint(10, size=N)\n",
    "\n",
    "loss, grads = model.loss(X, y)\n",
    "print 'Initial loss (no regularization): ', loss\n",
    "\n",
    "model.reg = 0.5\n",
    "loss, grads = model.loss(X, y)\n",
    "print 'Initial loss (with regularization): ', loss"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Gradient check\n",
    "After the loss looks reasonable, use numeric gradient checking to make sure that your backward pass is correct. When you use numeric gradient checking you should use a small amount of artifical data and a small number of neurons at each layer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "W1 max relative error: 5.802128e-04\n",
      "W2 max relative error: 1.002337e-02\n",
      "W3 max relative error: 1.465964e-04\n",
      "b1 max relative error: 1.496145e-05\n",
      "b2 max relative error: 1.524518e-06\n",
      "b3 max relative error: 1.256799e-09\n"
     ]
    }
   ],
   "source": [
    "num_inputs = 2\n",
    "input_dim = (3, 16, 16)\n",
    "reg = 0.0\n",
    "num_classes = 10\n",
    "X = np.random.randn(num_inputs, *input_dim)\n",
    "y = np.random.randint(num_classes, size=num_inputs)\n",
    "\n",
    "model = ThreeLayerConvNet(num_filters=3, filter_size=3,\n",
    "                          input_dim=input_dim, hidden_dim=7,\n",
    "                          dtype=np.float64)\n",
    "loss, grads = model.loss(X, y)\n",
    "for param_name in sorted(grads):\n",
    "    f = lambda _: model.loss(X, y)[0]\n",
    "    param_grad_num = eval_numerical_gradient(f, model.params[param_name], verbose=False, h=1e-6)\n",
    "    e = rel_error(param_grad_num, grads[param_name])\n",
    "    print '%s max relative error: %e' % (param_name, rel_error(param_grad_num, grads[param_name]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### From Bruce: How could Karpathy's code be so fast?! amazing!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Overfit small data\n",
    "A nice trick is to train your model with just a few training samples. You should be able to overfit small datasets, which will result in very high training accuracy and comparatively low validation accuracy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(Iteration 1 / 40) loss: 2.349523\n",
      "(Epoch 0 / 20) train acc: 0.210000; val_acc: 0.117000\n",
      "(Iteration 2 / 40) loss: 2.230899\n",
      "(Epoch 1 / 20) train acc: 0.390000; val_acc: 0.168000\n",
      "(Iteration 3 / 40) loss: 2.261169\n",
      "(Iteration 4 / 40) loss: 1.928003\n",
      "(Epoch 2 / 20) train acc: 0.390000; val_acc: 0.175000\n",
      "(Iteration 5 / 40) loss: 1.627280\n",
      "(Iteration 6 / 40) loss: 1.572219\n",
      "(Epoch 3 / 20) train acc: 0.560000; val_acc: 0.180000\n",
      "(Iteration 7 / 40) loss: 1.345524\n",
      "(Iteration 8 / 40) loss: 1.129340\n",
      "(Epoch 4 / 20) train acc: 0.720000; val_acc: 0.191000\n",
      "(Iteration 9 / 40) loss: 1.081076\n",
      "(Iteration 10 / 40) loss: 0.861047\n",
      "(Epoch 5 / 20) train acc: 0.740000; val_acc: 0.197000\n",
      "(Iteration 11 / 40) loss: 0.645798\n",
      "(Iteration 12 / 40) loss: 0.602214\n",
      "(Epoch 6 / 20) train acc: 0.790000; val_acc: 0.204000\n",
      "(Iteration 13 / 40) loss: 0.979241\n",
      "(Iteration 14 / 40) loss: 0.669964\n",
      "(Epoch 7 / 20) train acc: 0.830000; val_acc: 0.199000\n",
      "(Iteration 15 / 40) loss: 0.549550\n",
      "(Iteration 16 / 40) loss: 0.525081\n",
      "(Epoch 8 / 20) train acc: 0.880000; val_acc: 0.223000\n",
      "(Iteration 17 / 40) loss: 0.439530\n",
      "(Iteration 18 / 40) loss: 0.693792\n",
      "(Epoch 9 / 20) train acc: 0.900000; val_acc: 0.215000\n",
      "(Iteration 19 / 40) loss: 0.236789\n",
      "(Iteration 20 / 40) loss: 0.341065\n",
      "(Epoch 10 / 20) train acc: 0.960000; val_acc: 0.212000\n",
      "(Iteration 21 / 40) loss: 0.232828\n",
      "(Iteration 22 / 40) loss: 0.315352\n",
      "(Epoch 11 / 20) train acc: 0.970000; val_acc: 0.195000\n",
      "(Iteration 23 / 40) loss: 0.160931\n",
      "(Iteration 24 / 40) loss: 0.120117\n",
      "(Epoch 12 / 20) train acc: 0.970000; val_acc: 0.196000\n",
      "(Iteration 25 / 40) loss: 0.103993\n",
      "(Iteration 26 / 40) loss: 0.172213\n",
      "(Epoch 13 / 20) train acc: 1.000000; val_acc: 0.209000\n",
      "(Iteration 27 / 40) loss: 0.061338\n",
      "(Iteration 28 / 40) loss: 0.031649\n",
      "(Epoch 14 / 20) train acc: 1.000000; val_acc: 0.235000\n",
      "(Iteration 29 / 40) loss: 0.025087\n",
      "(Iteration 30 / 40) loss: 0.021599\n",
      "(Epoch 15 / 20) train acc: 0.970000; val_acc: 0.243000\n",
      "(Iteration 31 / 40) loss: 0.066156\n",
      "(Iteration 32 / 40) loss: 0.044681\n",
      "(Epoch 16 / 20) train acc: 0.990000; val_acc: 0.242000\n",
      "(Iteration 33 / 40) loss: 0.051806\n",
      "(Iteration 34 / 40) loss: 0.015075\n",
      "(Epoch 17 / 20) train acc: 0.990000; val_acc: 0.222000\n",
      "(Iteration 35 / 40) loss: 0.070566\n",
      "(Iteration 36 / 40) loss: 0.010064\n",
      "(Epoch 18 / 20) train acc: 1.000000; val_acc: 0.210000\n",
      "(Iteration 37 / 40) loss: 0.017410\n",
      "(Iteration 38 / 40) loss: 0.006208\n",
      "(Epoch 19 / 20) train acc: 0.990000; val_acc: 0.203000\n",
      "(Iteration 39 / 40) loss: 0.045483\n",
      "(Iteration 40 / 40) loss: 0.007360\n",
      "(Epoch 20 / 20) train acc: 1.000000; val_acc: 0.212000\n"
     ]
    }
   ],
   "source": [
    "num_train = 100\n",
    "small_data = {\n",
    "  'X_train': data['X_train'][:num_train],\n",
    "  'y_train': data['y_train'][:num_train],\n",
    "  'X_val': data['X_val'],\n",
    "  'y_val': data['y_val'],\n",
    "}\n",
    "\n",
    "model = ThreeLayerConvNet(weight_scale=1e-2)\n",
    "\n",
    "solver = Solver(model, small_data,\n",
    "                num_epochs=20, batch_size=50,\n",
    "                update_rule='adam',\n",
    "                optim_config={\n",
    "                  'learning_rate': 1e-4,\n",
    "                },\n",
    "                verbose=True, print_every=1)\n",
    "solver.train()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plotting the loss, training accuracy, and validation accuracy should show clear overfitting:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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JpVkEQUBHx9whUx/19BzP+9/fifscpk5NcM45MGMGbL55bWMVEZHy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\nk58AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9c8c6f8b10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplot(2, 1, 1)\n",
    "plt.plot(solver.loss_history, 'o')\n",
    "plt.xlabel('iteration')\n",
    "plt.ylabel('loss')\n",
    "\n",
    "plt.subplot(2, 1, 2)\n",
    "plt.plot(solver.train_acc_history, '-o')\n",
    "plt.plot(solver.val_acc_history, '-o')\n",
    "plt.legend(['train', 'val'], loc='upper left')\n",
    "plt.xlabel('epoch')\n",
    "plt.ylabel('accuracy')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train the net\n",
    "By training the three-layer convolutional network for one epoch, you should achieve greater than 40% accuracy on the training set:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(Iteration 1 / 980) loss: 2.304582\n",
      "(Epoch 0 / 1) train acc: 0.083000; val_acc: 0.119000\n",
      "(Iteration 21 / 980) loss: 1.992807\n",
      "(Iteration 41 / 980) loss: 2.057936\n",
      "(Iteration 61 / 980) loss: 1.700702\n",
      "(Iteration 81 / 980) loss: 1.739595\n",
      "(Iteration 101 / 980) loss: 1.820415\n",
      "(Iteration 121 / 980) loss: 1.783262\n",
      "(Iteration 141 / 980) loss: 1.530746\n",
      "(Iteration 161 / 980) loss: 1.391151\n",
      "(Iteration 181 / 980) loss: 1.728300\n",
      "(Iteration 201 / 980) loss: 1.666812\n",
      "(Iteration 221 / 980) loss: 1.471522\n",
      "(Iteration 241 / 980) loss: 1.477851\n",
      "(Iteration 261 / 980) loss: 1.300235\n",
      "(Iteration 281 / 980) loss: 1.758398\n",
      "(Iteration 301 / 980) loss: 1.211268\n",
      "(Iteration 321 / 980) loss: 1.343511\n",
      "(Iteration 341 / 980) loss: 1.777112\n",
      "(Iteration 361 / 980) loss: 1.265314\n",
      "(Iteration 381 / 980) loss: 1.145389\n",
      "(Iteration 401 / 980) loss: 1.346471\n",
      "(Iteration 421 / 980) loss: 1.295175\n",
      "(Iteration 441 / 980) loss: 1.283766\n",
      "(Iteration 461 / 980) loss: 1.586195\n",
      "(Iteration 481 / 980) loss: 1.433060\n",
      "(Iteration 501 / 980) loss: 1.745320\n",
      "(Iteration 521 / 980) loss: 1.592532\n",
      "(Iteration 541 / 980) loss: 1.199232\n",
      "(Iteration 561 / 980) loss: 1.482828\n",
      "(Iteration 581 / 980) loss: 1.414484\n",
      "(Iteration 601 / 980) loss: 1.396561\n",
      "(Iteration 621 / 980) loss: 1.273119\n",
      "(Iteration 641 / 980) loss: 1.168054\n",
      "(Iteration 661 / 980) loss: 1.407617\n",
      "(Iteration 681 / 980) loss: 1.345599\n",
      "(Iteration 701 / 980) loss: 1.599109\n",
      "(Iteration 721 / 980) loss: 1.280973\n",
      "(Iteration 741 / 980) loss: 1.185997\n",
      "(Iteration 761 / 980) loss: 1.263144\n",
      "(Iteration 781 / 980) loss: 1.191106\n",
      "(Iteration 801 / 980) loss: 1.429383\n",
      "(Iteration 821 / 980) loss: 1.554935\n",
      "(Iteration 841 / 980) loss: 1.462427\n",
      "(Iteration 861 / 980) loss: 1.271573\n",
      "(Iteration 881 / 980) loss: 1.206292\n",
      "(Iteration 901 / 980) loss: 1.294475\n",
      "(Iteration 921 / 980) loss: 1.477494\n",
      "(Iteration 941 / 980) loss: 1.460843\n",
      "(Iteration 961 / 980) loss: 1.214023\n",
      "(Epoch 1 / 1) train acc: 0.576000; val_acc: 0.566000\n"
     ]
    }
   ],
   "source": [
    "model = ThreeLayerConvNet(weight_scale=0.001, hidden_dim=500, reg=0.001)\n",
    "\n",
    "solver = Solver(model, data,\n",
    "                num_epochs=1, batch_size=50,\n",
    "                update_rule='adam',\n",
    "                optim_config={\n",
    "                  'learning_rate': 1e-4,\n",
    "                },\n",
    "                verbose=True, print_every=20)\n",
    "solver.train()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualize Filters\n",
    "You can visualize the first-layer convolutional filters from the trained network by running the following:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(32, 3, 7, 7)\n",
      "(32, 7, 7, 3)\n"
     ]
    }
   ],
   "source": [
    "print model.params['W1'].shape\n",
    "print model.params['W1'].transpose(0, 2, 3, 1).shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "?np.transpose"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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9TC3z+Syu8RzjpWur/MYGjwamMW/ybGIe323HfNx/DfPd8UHMU8v8vgXv3q5/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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9c8add3510>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from cs231n.vis_utils import visualize_grid\n",
    "\n",
    "grid = visualize_grid(model.params['W1'].transpose(0, 2, 3, 1))\n",
    "plt.imshow(grid.astype('uint8'))\n",
    "plt.axis('off')\n",
    "plt.gcf().set_size_inches(5, 5)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Spatial Batch Normalization\n",
    "We already saw that batch normalization is a very useful technique for training deep fully-connected networks. Batch normalization can also be used for convolutional networks, but we need to tweak it a bit; the modification will be called \"spatial batch normalization.\"\n",
    "\n",
    "Normally batch-normalization accepts inputs of shape `(N, D)` and produces outputs of shape `(N, D)`, where we normalize across the minibatch dimension `N`. For data coming from convolutional layers, batch normalization needs to accept inputs of shape `(N, C, H, W)` and produce outputs of shape `(N, C, H, W)` where the `N` dimension gives the minibatch size and the `(H, W)` dimensions give the spatial size of the feature map.\n",
    "\n",
    "If the feature map was produced using convolutions, then we expect the statistics of each feature channel to be relatively consistent both between different imagesand different locations within the same image. Therefore spatial batch normalization computes a mean and variance for each of the `C` feature channels by computing statistics over both the minibatch dimension `N` and the spatial dimensions `H` and `W`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Spatial batch normalization: forward\n",
    "\n",
    "In the file `cs231n/layers.py`, implement the forward pass for spatial batch normalization in the function `spatial_batchnorm_forward`. Check your implementation by running the following:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Before spatial batch normalization:\n",
      "  Shape:  (2, 3, 4, 5)\n",
      "  Means:  [ 10.54151492   8.97460893   8.25908616]\n",
      "  Stds:  [ 3.44531743  4.11144469  4.44106864]\n",
      "After spatial batch normalization:\n",
      "  Shape:  (2, 3, 4, 5)\n",
      "  Means:  [  3.14852311e-17   4.12170298e-16   4.16333634e-16]\n",
      "  Stds:  [ 0.99999958  0.9999997   0.99999975]\n",
      "After spatial batch normalization (nontrivial gamma, beta):\n",
      "  Shape:  (2, 3, 4, 5)\n",
      "  Means:  [ 6.  7.  8.]\n",
      "  Stds:  [ 2.99999874  3.99999882  4.99999873]\n"
     ]
    }
   ],
   "source": [
    "# Check the training-time forward pass by checking means and variances\n",
    "# of features both before and after spatial batch normalization\n",
    "\n",
    "N, C, H, W = 2, 3, 4, 5\n",
    "x = 4 * np.random.randn(N, C, H, W) + 10\n",
    "\n",
    "print 'Before spatial batch normalization:'\n",
    "print '  Shape: ', x.shape\n",
    "print '  Means: ', x.mean(axis=(0, 2, 3))\n",
    "print '  Stds: ', x.std(axis=(0, 2, 3))\n",
    "\n",
    "# Means should be close to zero and stds close to one\n",
    "gamma, beta = np.ones(C), np.zeros(C)\n",
    "bn_param = {'mode': 'train'}\n",
    "out, _ = spatial_batchnorm_forward(x, gamma, beta, bn_param)\n",
    "print 'After spatial batch normalization:'\n",
    "print '  Shape: ', out.shape\n",
    "print '  Means: ', out.mean(axis=(0, 2, 3))\n",
    "print '  Stds: ', out.std(axis=(0, 2, 3))\n",
    "\n",
    "# Means should be close to beta and stds close to gamma\n",
    "gamma, beta = np.asarray([3, 4, 5]), np.asarray([6, 7, 8])\n",
    "out, _ = spatial_batchnorm_forward(x, gamma, beta, bn_param)\n",
    "print 'After spatial batch normalization (nontrivial gamma, beta):'\n",
    "print '  Shape: ', out.shape\n",
    "print '  Means: ', out.mean(axis=(0, 2, 3))\n",
    "print '  Stds: ', out.std(axis=(0, 2, 3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "After spatial batch normalization (test-time):\n",
      "  means:  [ 0.02550061  0.03272305 -0.00285469 -0.01002203]\n",
      "  stds:  [ 0.98911777  1.00719847  0.99838812  0.99219505]\n"
     ]
    }
   ],
   "source": [
    "# Check the test-time forward pass by running the training-time\n",
    "# forward pass many times to warm up the running averages, and then\n",
    "# checking the means and variances of activations after a test-time\n",
    "# forward pass.\n",
    "\n",
    "N, C, H, W = 10, 4, 11, 12\n",
    "\n",
    "bn_param = {'mode': 'train'}\n",
    "gamma = np.ones(C)\n",
    "beta = np.zeros(C)\n",
    "for t in xrange(50):\n",
    "  x = 2.3 * np.random.randn(N, C, H, W) + 13\n",
    "  spatial_batchnorm_forward(x, gamma, beta, bn_param)\n",
    "bn_param['mode'] = 'test'\n",
    "x = 2.3 * np.random.randn(N, C, H, W) + 13\n",
    "a_norm, _ = spatial_batchnorm_forward(x, gamma, beta, bn_param)\n",
    "\n",
    "# Means should be close to zero and stds close to one, but will be\n",
    "# noisier than training-time forward passes.\n",
    "print 'After spatial batch normalization (test-time):'\n",
    "print '  means: ', a_norm.mean(axis=(0, 2, 3))\n",
    "print '  stds: ', a_norm.std(axis=(0, 2, 3))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Spatial batch normalization: backward\n",
    "In the file `cs231n/layers.py`, implement the backward pass for spatial batch normalization in the function `spatial_batchnorm_backward`. Run the following to check your implementation using a numeric gradient check:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dx error:  2.3125279784e-08\n",
      "dgamma error:  2.97977202933e-11\n",
      "dbeta error:  3.27579169887e-12\n"
     ]
    }
   ],
   "source": [
    "N, C, H, W = 2, 3, 4, 5\n",
    "x = 5 * np.random.randn(N, C, H, W) + 12\n",
    "gamma = np.random.randn(C)\n",
    "beta = np.random.randn(C)\n",
    "dout = np.random.randn(N, C, H, W)\n",
    "\n",
    "bn_param = {'mode': 'train'}\n",
    "fx = lambda x: spatial_batchnorm_forward(x, gamma, beta, bn_param)[0]\n",
    "fg = lambda a: spatial_batchnorm_forward(x, gamma, beta, bn_param)[0]\n",
    "fb = lambda b: spatial_batchnorm_forward(x, gamma, beta, bn_param)[0]\n",
    "\n",
    "dx_num = eval_numerical_gradient_array(fx, x, dout)\n",
    "da_num = eval_numerical_gradient_array(fg, gamma, dout)\n",
    "db_num = eval_numerical_gradient_array(fb, beta, dout)\n",
    "\n",
    "_, cache = spatial_batchnorm_forward(x, gamma, beta, bn_param)\n",
    "dx, dgamma, dbeta = spatial_batchnorm_backward(dout, cache)\n",
    "print 'dx error: ', rel_error(dx_num, dx)\n",
    "print 'dgamma error: ', rel_error(da_num, dgamma)\n",
    "print 'dbeta error: ', rel_error(db_num, dbeta)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Experiment!\n",
    "Experiment and try to get the best performance that you can on CIFAR-10 using a ConvNet. Here are some ideas to get you started:\n",
    "\n",
    "### Things you should try:\n",
    "- Filter size: Above we used 7x7; this makes pretty pictures but smaller filters may be more efficient\n",
    "- Number of filters: Above we used 32 filters. Do more or fewer do better?\n",
    "- Batch normalization: Try adding spatial batch normalization after convolution layers and vanilla batch normalization after affine layers. Do your networks train faster?\n",
    "- Network architecture: The network above has two layers of trainable parameters. Can you do better with a deeper network? You can implement alternative architectures in the file `cs231n/classifiers/convnet.py`. Some good architectures to try include:\n",
    "    - [conv-relu-pool]xN - conv - relu - [affine]xM - [softmax or SVM]\n",
    "    - [conv-relu-pool]XN - [affine]XM - [softmax or SVM]\n",
    "    - [conv-relu-conv-relu-pool]xN - [affine]xM - [softmax or SVM]\n",
    "\n",
    "### Tips for training\n",
    "For each network architecture that you try, you should tune the learning rate and regularization strength. When doing this there are a couple important things to keep in mind:\n",
    "\n",
    "- If the parameters are working well, you should see improvement within a few hundred iterations\n",
    "- Remember the course-to-fine approach for hyperparameter tuning: start by testing a large range of hyperparameters for just a few training iterations to find the combinations of parameters that are working at all.\n",
    "- Once you have found some sets of parameters that seem to work, search more finely around these parameters. You may need to train for more epochs.\n",
    "\n",
    "### Going above and beyond\n",
    "If you are feeling adventurous there are many other features you can implement to try and improve your performance. You are **not required** to implement any of these; however they would be good things to try for extra credit.\n",
    "\n",
    "- Alternative update steps: For the assignment we implemented SGD+momentum, RMSprop, and Adam; you could try alternatives like AdaGrad or AdaDelta.\n",
    "- Alternative activation functions such as leaky ReLU, parametric ReLU, or MaxOut.\n",
    "- Model ensembles\n",
    "- Data augmentation\n",
    "\n",
    "If you do decide to implement something extra, clearly describe it in the \"Extra Credit Description\" cell below.\n",
    "\n",
    "### What we expect\n",
    "At the very least, you should be able to train a ConvNet that gets at least 65% accuracy on the validation set. This is just a lower bound - if you are careful it should be possible to get accuracies much higher than that! Extra credit points will be awarded for particularly high-scoring models or unique approaches.\n",
    "\n",
    "You should use the space below to experiment and train your network. The final cell in this notebook should contain the training, validation, and test set accuracies for your final trained network. In this notebook you should also write an explanation of what you did, any additional features that you implemented, and any visualizations or graphs that you make in the process of training and evaluating your network.\n",
    "\n",
    "Have fun and happy training!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Train a really good model on CIFAR-10"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Extra Credit Description\n",
    "If you implement any additional features for extra credit, clearly describe them here with pointers to any code in this or other files if applicable."
   ]
  }
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